@Article{Guven:2011:WRM,
author = "Aytac Guven and Ozgur Kisi",
title = "Estimation of Suspended Sediment Yield in Natural
Rivers Using Machine-coded Linear Genetic Programming",
journal = "Water Resources Management",
year = "2011",
volume = "25",
number = "2",
pages = "691--704",
month = jan,
keywords = "genetic algorithms, genetic programming, gene
expression programming, Suspended sediment yield,
Modelling, Linear genetic programming, ANN, Neural
networks",
publisher = "Springer",
ISSN = "0920-4741",
DOI = "doi:10.1007/s11269-010-9721-x",
size = "14 pages",
abstract = "Estimation of suspended sediment yield is subject to
uncertainty and bias. Many methods have been developed
for estimating sediment yield but they still lack
accuracy and robustness. This paper investigates the
use of a machine-coded linear genetic programming (LGP)
in daily suspended sediment estimation. The accuracy of
LGP is compared with those of the Gene-expression
programming (GEP), which is another branch of GP, and
artificial neural network (ANN) technique. Daily
streamflow and suspended sediment data from two
stations on the Tongue River in Montana, USA, are used
as case studies. Root mean square error (RMSE) and
determination coefficient (R2) statistics are used for
evaluating the accuracy of the models. Based on the
comparison of the results, it is found that the LGP
performs better than the GEP and ANN techniques. The
GEP was also found to be better than the ANN. For the
upstream and downstream stations, it is found that the
LGP models with RMSE = 175 ton/day, R2 = 0.941 and RMSE
= 254 ton/day, R2 = 0.959 in test period is superior in
estimating daily suspended sediments than the best
accurate GEP model with RMSE = 231 ton/day, R2 = 0.941
and RMSE = 331 ton/day, R2 = 0.934, respectively.",
affiliation = "Civil Engineering Department, Hydraulics Division,
Gaziantep University, 27310 Gaziantep, Turkey",
}